[Corpora-List] 1st CFP: Deep Learning and Formal Languages: Building Bridges (workshop @ACL)

Matthias Gallé mgalle at gmail.com
Thu Dec 20 21:55:53 CET 2018


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Deep Learning and Formal Languages: Building Bridges

Deep Learning and Formal Languages: Building Bridges -- ACL 2019 Workshop

Florence, Italy

Website: https://sites.google.com/view/delfol-workshop-acl19

SUBMISSION DEADLINE: 26 April 2019

While deep learning and neural networks have revolutionized the field of natural language processing, changed the habits of its practitioners and opened up new research directions, many aspects of the inner workings of deep neural networks remain unknown.

At the same time, we have access to many decades of accumulated knowledge on formal languages, grammar, and transductions, both weighted and unweighted and for strings as well as trees: closure properties, computational complexity of various operations, relationships between various classes of them, and many empirical and theoretical results on their learnability.

The goal of this workshop is to bring researchers together who are interested in how our understanding of formal languages can contribute to the understanding and design of neural network architectures for natural language processing. For example, fundamental work on neural nets has examined whether they could learn different classes of formal languages, and reciprocally whether formal grammars or automata could closely approximate neural networks. Recently we have seen new research directions on what each formalism can bring to understand or improve the other. Topics which fall within the purview of the workshop include, but are not limited to

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Learnability of formal languages with neural nets (both strong and weak

learning)

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Relationship between deep learning models and linguistically inspired

formalisms

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Connections between neural network architectures and classical

computational models

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Traditional formal grammars augmented through non-linearity

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Hybrid models combining neural networks and finite state machines

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The use of formal grammars to analyze and interpret the behavior of

neural networks

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Approximating neural networks with weighted automata and grammars

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Including formal grammar constraints as symbolic priors in neural

networks

We call for three types of papers:

(1) Regular workshop paper

(2) Extended abstracts

(3) Cross-submissions

Only (1) will be included in the workshop proceedings

Some recent work which falls within the scope of this call include:

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Bridging CNNs, RNNs, and Weighted Finite-State Machines. Roy Schwartz,

Sam Thomson,and Noah A Smith. (ACL 2018)

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Rational Recurrences. Hao Peng, Roy Schwartz, Sam Thomson, Noah A.

Smith. (ENMLP 2018)

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Recurrent Neural Networks as Weighted Language Recognizers. Y. Chen, S.

Gilroy, A. Maletti, J. May, and K. Knight. (NAACL 2018)

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Using Regular Languages to Explore the Representational Capacity of

Recurrent Neural Architectures. Abhijit Mahalunkar and John D. Kelleher.

(ICANN 2018)

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Explaining black boxes on sequential data using weighted automata.

Stéphane Ayache, Rémi Eyraud <http://www.lif.univ-mrs.fr/~reyraud> and

Noé Goudian. (ICGI 2018)

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Extracting Automata from Recurrent Neural Networks Using Queries and

Counterexamples. Gail Weiss, Yoav Goldberg, and Eran Yahav. (ICML 2018)

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Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data

for Future Prediction. Siyuan Qi, Baoxiong Jia, and Song-Chun Zhu. (ICML

2018)

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Efficient Gradient Computation for Structured Output Learning with

Rational and Tropical Losses. Corinna Cortes, Vitaly Kuznetsov, Mehryar

Mohri, Dmitry Storcheus, Scott Yang (NIPS 2018)

-

Composing RNNs and FSTs for Small Data: Recovering Missing Characters in

Old Hawaiian Text. Oiwi Parker Jones and Brendan Shillingford (IRASL

workshop at NIPS 2018)

-

Verification of Recurrent Neural Networks Through Rule Extraction. Q

Wang, K Zhang, X Liu, and CL Giles (arxiv.org 2018)

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A Comparison of Rule Extraction for Different Recurrent Neural Network

Models and Grammatical Complexity. Q Wang, K Zhang, II Ororbia, G

Alexander, X Xing, X Liu, CL Giles (arxiv.org 2018)

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Grammar Variational Autoencoder. Matt J. Kusner, Brooks Paige, José

Miguel Hernández-Lobato. (ICML 2017)

-

Subregular Complexity and Deep Learning. Enes Avcu, Chihiro Shibata, and

Jeffrey Heinz. (LAML 2017)

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Recurrent Neural Network Grammars. Chris Dyer, Adhiguna Kuncoro, Miguel

Ballesteros, and Noah A. Smith. (NAACL 2016).

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Weighting finite-state transductions with neural context. Pushpendre

Rastogi, Ryan Cotterell, and Jason Eisner (NAACL 2016)

Programme Committee

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Borja Balle, Amazon

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Xavier Carreras, dMetrics

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Shay B. Cohen, University of Edinburgh

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Alex Clark, University of London

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Ewan Dunbar, Université Paris Diderot

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Marc Dymetman, Naver Labs Europe

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Kyle Gorman, City University of New York

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Hadrien Glaude, Amazon

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John Hale, University of Georgia

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Mans Hulden, University of Colorado

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Franco Luque, University of Córdoba

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Chihiro Shibata, Tokyo University of Technology

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Adina Williams, FAIR

Organizers

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Jason Eisner, Johns Hopkins University

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Matthias Gallé, Naver Labs Europe

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Jeffrey Heinz, Stony Brook University

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Ariadna Quattoni, dMetrics

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